Hyperspectral imaging, a rapidly evolving field, has witnessed the ascendancy of deep learning techniques, supplanting classical feature extraction and classification methods in various applications. However, many researchers employ arbitrary architectures for hyperspectral image processing, often without rigorous analysis of the interplay between spectral and spatial information. This oversight neglects the implications of combining these two modalities on model performance. In this paper, we evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation. Our analysis disentangles the impact of different architectures, spanning various spectral and spatial granularities. Specifically, we investigate the effects of spectral resolution (capturing spectral information) and spatial texture (conveying spatial details) on segmentation outcomes. Additionally, we explore the transferability of knowledge from large pre-trained image foundation models, originally designed for RGB images, to the hyperspectral domain. Results show that incorporating spatial information alongside spectral data leads to improved segmentation results, and that it is essential to further work on novel architectures comprising spectral and spatial information and on the adaption of RGB foundation models into the hyperspectral domain. Furthermore, we contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset. This dataset contains different non-ferrous fractions of Waste Electrical and Electronic Equipment (WEEE), including Copper, Brass, Aluminum, Stainless Steel, and White Copper, spanning the range of 400 to 1000 nm. We expect these conclusions can guide novel researchers in the field of hyperspectral imaging.

翻译：高光谱成像作为一个快速发展的领域，已见证了深度学习技术在多类应用中的崛起，并逐步取代了传统的特征提取与分类方法。然而，许多研究者采用任意架构处理高光谱图像，往往缺乏对光谱与空间信息交互作用的严谨分析。这种疏忽忽略了结合这两种模态对模型性能的影响。本文评估了多种深度学习架构在高光谱图像分割任务中的表现。我们的分析解耦了不同架构的影响，涵盖了多种光谱与空间粒度。具体而言，我们研究了光谱分辨率（捕获光谱信息）与空间纹理（传递空间细节）对分割结果的影响。此外，我们探索了从大型预训练图像基础模型（原设计用于RGB图像）向高光谱领域进行知识迁移的可能性。实验结果表明，在光谱数据中融入空间信息能够提升分割性能，且有必要进一步研究包含光谱与空间信息的新型架构，以及将RGB基础模型适配至高光谱领域的工作。此外，我们通过清理并公开发布Tecnalia WEEE高光谱数据集，为该领域做出贡献。该数据集包含电子电气设备废弃物（WEEE）的不同有色金属组分，涵盖铜、黄铜、铝、不锈钢和白铜，光谱范围覆盖400至1000纳米。我们希望这些结论能够为高光谱成像领域的新研究者提供指导。